Bibliographic record
Abstract
ACSM’s Publications Committee is pleased to highlight the excellent content its journals are publishing and acknowledges authors whose exemplary work provides readers with unique scientific and practical content. The editorial team of ACSM’s Health & Fitness Journal® has selected “Developing a Lifelong Resistance Training Program,” by Len R. Kravitz, Ph.D. (1), as the 2019 Paper of the Year. Resistance training has been identified as an evidence-based, effective, and vital modality for enhancing health-related physical fitness and function and a recommended component of comprehensive exercise programs for people of all ages (2–5). Len’s article was selected because it has so clearly translated the scientific literature and industry guidelines on resistance training into practical information, which readers can apply immediately with clients and patients. Len distinctly summarizes current science behind the recommendations and provides meaningful, practical examples that readers can easily follow. His descriptions of best practices are consistent with recommendations for the general population (2,3), athletes (4), youth (3,5), older adults (6–9), and select populations (10–13). He provides easy to follow recommendations for designing safe and effective resistance training programs for clients and patients throughout the life span. Students, faculty, health and fitness professionals, and ACSM certification candidates can use this article as a primer. Len is an accomplished, articulate, and highly sought health and fitness industry educator and expert. Len’s articles and keynote presentations are always exceptionally informative, practical, and engaging. His knowledge and expertise as a scientist, researcher, and author is second only to his energy, enthusiasm, and passion for teaching. Len is program coordinator of Exercise Science and a researcher at the University of New Mexico where he won the Outstanding Teacher of the Year award. For his distinguished service, Len was inducted into the National Health and Fitness Hall of Fame Museum and Institute and has been recognized as the 2009 Canadian Fitness Professional “Specialty Presenter of the Year,” American Council on Exercise (ACE) 2006 “Fitness Educator of the Year,” and has received the Canadian Fitness Professional “Lifetime Achievement Award.” To hear more about this article from Len, click here: https://links.lww.com/FIT/A147. To read Len's Paper of the Year visit: https://journals.lww.com/acsm-healthfitness/Fulltext/2019/01000/DEVELOPING_A_LIFELONG_RESISTANCE_TRAINING_PROGRAM.6.aspx.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.402 | 0.275 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".